Anaconda vs. Jupyter Notebook vs. Microsoft Visual Studio Code

I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.

Incentivized

I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

Incentivized

As a general workhorse IDE, Microsoft Visual Studio Codee is unmatched. Building on the early success of applications such as Atom, it has long been the standard for electron based IDEs. It can be outshone using IDEs that are dedicated to particular platforms, such as Microsoft Visual Studio Code for .net and the Jetbrains IDEs for Java, Python and others. For remote collaborative development, something like Zed is ahead of VSCode live share, which can be quite flakey.

Incentivized

• Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
• Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
• Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.

Incentivized

• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

Incentivized

• Integrate with the git repository very well.
• Integrated copilot chat is very helpful to write code snippets and helps beginners to start with coding and development.
• Great library of available extensions is one of the best features in Microsoft Visual Studio Code.
• Dedicated Testing option to configure pytest and others are quite handy to use.

Incentivized

• It can have a cloud interface to store the work.
• Compatible for large size files.
• I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.

Incentivized

• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

Incentivized

• The customization of key combinations should be more accessible and easier to change
• The auxiliary panels could be minimized or as floating tabs which are displayed when you click on them
• A monitoring panel of resources used by Microsoft Visual Studio Code or plugins and extensions would help a lot to be able to detect any malfunction of these

Incentivized

It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.

Incentivized

Solid tool that provides everything you need to develop most types of applications. The only reason not a 10 is that if you are doing large distributed teams on Enterprise level, Professional does provide more tools to support that and would be worth the cost.

Incentivized

I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.

Incentivized

Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

Incentivized

Microsoft Visual Studio Code earns a 10 for its exceptional balance of power and simplicity. Its intuitive interface, robust extension ecosystem, and integrated terminal streamline development. With seamless Git integration and highly customizable settings, it adapts perfectly to any workflow, making complex coding tasks feel effortless for beginners and experts alike.

Incentivized

Overall, Microsoft Visual Studio Code is pretty reliable. Every so often, though, the app will experience an unexplained crash. Since it is a stand-alone app, connectivity or service issues don't occur in my experience. Restarting the app seems to always get around the problem, but I do make sure to save and backup current work.

Incentivized

Microsoft Visual Studio Code is pretty snappy in performance terms. It launches quickly, and tasks are performed quickly. I don't have a lot of integrations other than CoPilot, but I suspect that if the integration partner is provisioned appropriately that any performance impact would be pretty minimal. It doesn't have a lot of bells and whistles (unless you start adding plugins left and right).

Incentivized

Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.

Incentivized

I haven't had a need to contact support. However, all required help is out there in public forums.

Incentivized

Active development means filing a bug on the GitHub repo typically gets you a response within 4 days. There are plugins for almost everything you need, whether it be linting, Vim emulation, even language servers (which I use to code in Scala). There is well-maintained official documentation. The only thing missing is forums. The closest thing is GitHub issues, which typically has the answers but is hard to sift through -- there are currently 78k issues.

Incentivized

I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!

Incentivized

With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

Incentivized

Visual Studio Code stacks up nicely against Visual Studio because of the price and because it can be installed without admin rights. We don't exclusively use Visual Studio Code, but rather use Visual Studio and Visual Studio code depending on the project and which version of source control the given project is wired up to.

Incentivized

It is easily deployed with our Jamf Pro instance. There is actually very little setup involved in getting the app deployed, and it is fairly well self-contained and does not deploy a large amount of associated files. However, it is not particularly conducive to large project, multi-developer/department projects that involve some form of central integration.

Incentivized

• It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
• By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
• There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.

Incentivized

• Positive impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

Incentivized

• Saves money by replacing suites of tools such as Visual Studio, IntelliJ, etc.
• Speeds development time and developer environment setup time
• Strengthens code quality with integrated autoformatting and linting
• Strengths Git practices by keeping version control tightly connected with the code

Incentivized